OpenRaiser/Intern-Atlas huggingface dataset:
Time Reversal based MAC for Multi-Hop Underwater Acoustic Networks
Venue: arXiv cs.NI 2019
Year: 2019

Constrained-energy underwater acoustic nodes are typically connected via a multi-hop underwater acoustic network (MHUAN) to cover a broad marine region. Recently, protocols for efficiently connecting such nodes have received considerable attention. In this paper, we show that the time reversal (TR) process plays an important role in the medium access control (MAC) because of its physical capability to exploit the multi-path energy from the richly scattering underwater environment, as well as to focus the signal energy in both spatial and temporal domains. In MHUANs, with severe multi-path propagation at the physical layer, the active TR process spatially focuses the signals to the location of the intended receiver; this significantly diminishes the interference among parallel links. We propose an active TR-based MAC protocol for MHUANs, with the aim of minimizing collision and maximizing channel utilization simultaneously. Furthermore, by considering the impact of the cross-correlation between different links on the TR-based medium access, we derive the threshold of the link cross-correlation to resolve collision caused by the high cross-correlation between realistic links. We perform simulations using the OPNET and BELLHOP environments, and show that the proposed TR-based MAC results in significantly improved throughput, decreased delay, and reduced data drop ratio in MHUANs.
Automatically Generating Documentation for Lambda Expressions in Java
Venue: arXiv cs.SE 2019
Year: 2019

When lambda expressions were introduced to the Java programming language as part of the release of Java 8 in 2014, they were the language's first step into functional programming. Since lambda expressions are still relatively new, not all developers use or understand them. In this paper, we first present the results of an empirical study to determine how frequently developers of GitHub repositories make use of lambda expressions and how they are documented. We find that 11% of Java GitHub repositories use lambda expressions, and that only 6% of the lambda expressions are accompanied by source code comments. We then present a tool called LambdaDoc which can automatically detect lambda expressions in a Java repository and generate natural language documentation for them. Our evaluation of LambdaDoc with 23 professional developers shows that they perceive the generated documentation to be complete, concise, and expressive, while the majority of the documentation produced by our participants without tool support was inadequate. Our contribution builds an important step towards automatically generating documentation for functional programming constructs in an object-oriented language.
A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

This paper extends off-policy reinforcement learning to the multi-agent case in which a set of networked agents communicating with their neighbors according to a time-varying graph collaboratively evaluates and improves a target policy while following a distinct behavior policy. To this end, the paper develops a multi-agent version of emphatic temporal difference learning for off-policy policy evaluation, and proves convergence under linear function approximation. The paper then leverages this result, in conjunction with a novel multi-agent off-policy policy gradient theorem and recent work in both multi-agent on-policy and single-agent off-policy actor-critic methods, to develop and give convergence guarantees for a new multi-agent off-policy actor-critic algorithm.
Pricing and Routing Mechanisms for Differentiated Services in an Electric Vehicle Public Charging Station Network
Venue: arXiv cs.SY 2019
Year: 2019

We consider a Charging Network Operator (CNO) that owns a network of Electric Vehicle (EV) public charging stations and wishes to offer a menu of differentiated service options for access to its stations. This involves designing optimal pricing and routing schemes for the setting where users cannot directly choose which station they use. Instead, they choose their priority level and energy request amount from the differentiated service menu, and then the CNO directly assigns them to a station on their path. This allows higher priority users to experience lower wait times at stations, and allows the CNO to directly manage demand, exerting a higher level of control that can be used to manage the effect of EV on the grid and control station wait times. We consider the scenarios where the CNO is a social welfare-maximizing or a profit-maximizing entity, and in both cases, design pricing-routing policies that ensure users reveal their true parameters to the CNO.
DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance
Venue: arXiv cs.CV 2019
Year: 2019

Depth estimation is an important capability for autonomous vehicles to understand and reconstruct 3D environments as well as avoid obstacles during the execution. Accurate depth sensors such as LiDARs are often heavy, expensive and can only provide sparse depth while lighter depth sensors such as stereo cameras are noiser in comparison. We propose an end-to-end learning algorithm that is capable of using sparse, noisy input depth for refinement and depth completion. Our model also produces the camera pose as a byproduct, making it a great solution for autonomous systems. We evaluate our approach on both indoor and outdoor datasets. Empirical results show that our method performs well on the KITTI~\cite{kitti_geiger2012we} dataset when compared to other competing methods, while having superior performance in dealing with sparse, noisy input depth on the TUM~\cite{sturm12iros} dataset.
Hash functions from superspecial genus-2 curves using Richelot isogenies
Venue: arXiv cs.CR 2019
Year: 2019

Last year Takashima proposed a version of Charles, Goren and Lauter's hash function using Richelot isogenies, starting from a genus-2 curve that allows for all subsequent arithmetic to be performed over a quadratic finite field Fp2. In a very recent paper Flynn and Ti point out that Takashima's hash function is insecure due to the existence of small isogeny cycles. We revisit the construction and show that it can be repaired by imposing a simple restriction, which moreover clarifies the security analysis. The runtime of the resulting hash function is dominated by the extraction of 3 square roots for every block of 3 bits of the message, as compared to one square root per bit in the elliptic curve case; however in our setting the extractions can be parallelized and are done in a finite field whose bit size is reduced by a factor 3. Along the way we argue that the full supersingular isogeny graph is the wrong context in which to study higher-dimensional analogues of Charles, Goren and Lauter's hash function, and advocate the use of the superspecial subgraph, which is the natural framework in which to view Takashima's Fp2-friendly starting curve.
Content Differences in Syntactic and Semantic Representations
Venue: arXiv cs.CL 2019
Year: 2019

Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. The debate has been constrained by the scarcity of empirical comparative studies between syntactic and semantic schemes, which hinders the development of parsing methods informed by the details of target schemes and constructions. We target this gap, and take Universal Dependencies (UD) and UCCA as a test case. After abstracting away from differences of convention or formalism, we find that most content divergences can be ascribed to: (1) UCCA's distinction between a Scene and a non-Scene; (2) UCCA's distinction between primary relations, secondary ones and participants; (3) different treatment of multi-word expressions, and (4) different treatment of inter-clause linkage. We further discuss the long tail of cases where the two schemes take markedly different approaches. Finally, we show that the proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement. The substantial differences between the schemes suggest that semantic parsers are likely to benefit downstream text understanding applications beyond their syntactic counterparts.
Inferring demand from partially observed data to address the mismatch between demand and supply of taxis in the presence of rain
Venue: arXiv cs.CY 2019
Year: 2019

Analyzing mismatch in supply and demand of taxis is an important effort to understand passengers' demand. In this paper, we have analyzed the effect of rain on the demand for yellow taxis in city-wide as well as in a point of interest in New York City. Because a pickup event is a realized demand, we studied empty travel time, the number of pickups per driver, the average amount of income per drive indices to infer demand from taxis data of 2013. Findings highlight that the higher demand exists because of many short-trips during the rain. This paper illustrates the change in passengers' demand increased by the onset of weather condition.
Development details and computational benchmarking of DEPAM
Venue: arXiv cs.DC 2019
Year: 2019

In the big data era of observational oceanography, passive acoustics datasets are becoming too high volume to be processed on local computers due to their processor and memory limitations. As a result there is a current need for our community to turn to cloud-based distributed computing. We present a scalable computing system for FFT (Fast Fourier Transform)-based features (e.g., Power Spectral Density) based on the Apache distributed frameworks Hadoop and Spark. These features are at the core of many different types of acoustic analysis where the need of processing data at scale with speed is evident, e.g. serving as long-term averaged learning representations of soundscapes to identify periods of acoustic interest. In addition to provide a complete description of our system implementation, we also performed a computational benchmark comparing our system to three other Scala-only, Matlab and Python based systems in standalone executions, and evaluated its scalability using the speed up metric. Our current results are very promising in terms of computational performance, as we show that our proposed Hadoop/Spark system performs reasonably well on a single node setup comparatively to state-of-the-art processing tools used by the PAM community, and that it could also fully leverage more intensive cluster resources with a almost-linear scalability behaviour above a certain dataset volume.
Live Reconstruction of Large-Scale Dynamic Outdoor Worlds
Venue: arXiv cs.CV 2019
Year: 2019

Standard 3D reconstruction pipelines assume stationary world, therefore suffer from `ghost artifacts' whenever dynamic objects are present in the scene. Recent approaches has started tackling this issue, however, they typically either only discard dynamic information, represent it using bounding boxes or per-frame depth or rely on approaches that are inherently slow and not suitable to online settings. We propose an end-to-end system for live reconstruction of large-scale outdoor dynamic environments. We leverage recent advances in computationally efficient data-driven approaches for 6-DoF object pose estimation to segment the scene into objects and stationary `background'. This allows us to represent the scene using a time-dependent (dynamic) map, in which each object is explicitly represented as a separate instance and reconstructed in its own volume. For each time step, our dynamic map maintains a relative pose of each volume with respect to the stationary background. Our system operates in incremental manner which is essential for on-line reconstruction, handles large-scale environments with objects at large distances and runs in (near) real-time. We demonstrate the efficacy of our approach on the KITTI dataset, and provide qualitative and quantitative results showing high-quality dense 3D reconstructions of a number of dynamic scenes.
Predicting Academic Performance for College Students: A Campus Behavior Perspective
Venue: arXiv cs.CY 2019
Year: 2019

Detecting abnormal behaviors of students in time and providing personalized intervention and guidance at the early stage is important in educational management. Academic performance prediction is an important building block to enabling this pre-intervention and guidance. Most of the previous studies are based on questionnaire surveys and self-reports, which suffer from small sample size and social desirability bias. In this paper, we collect longitudinal behavioral data from 6,597 students' smart cards and propose three major types of discriminative behavioral factors, diligence, orderliness, and sleep patterns. Empirical analysis demonstrates these behavioral factors are strongly correlated with academic performance. Furthermore, motivated by social influence theory, we analyze the correlation between each student's academic performance with his/her behaviorally similar students'. Statistical tests indicate this correlation is significant. Based on these factors, we further build a multi-task predictive framework based on a learning-to-rank algorithm for academic performance prediction. This framework captures inter-semester correlation, inter-major correlation and integrates student similarity to predict students' academic performance. The experiments on a large-scale real-world dataset show the effectiveness of our methods for predicting academic performance and the effectiveness of proposed behavioral factors.
A Machine Learning Approach to Comment Toxicity Classification
Venue: arXiv cs.CL 2019
Year: 2019

Now-a-days, derogatory comments are often made by one another, not only in offline environment but also immensely in online environments like social networking websites and online communities. So, an Identification combined with Prevention System in all social networking websites and applications, including all the communities, existing in the digital world is a necessity. In such a system, the Identification Block should identify any negative online behaviour and should signal the Prevention Block to take action accordingly. This study aims to analyse any piece of text and detecting different types of toxicity like obscenity, threats, insults and identity-based hatred. The labelled Wikipedia Comment Dataset prepared by Jigsaw is used for the purpose. A 6-headed Machine Learning tf-idf Model has been made and trained separately, yielding a Mean Validation Accuracy of 98.08% and Absolute Validation Accuracy of 91.61%. Such an Automated System should be deployed for enhancing healthy online conversation
A class of narrow-sense BCH codes over $\mathbb{F}_q$ of length $\frac{q^m-1}{2}$
Venue: arXiv cs.IT 2019
Year: 2019

BCH codes with efficient encoding and decoding algorithms have many applications in communications, cryptography and combinatorics design. This paper studies a class of linear codes of length $ \frac{q^m-1}{2}$ over $\mathbb{F}_q$ with special trace representation, where $q$ is an odd prime power. With the help of the inner distributions of some subsets of association schemes from bilinear forms associated with quadratic forms, we determine the weight enumerators of these codes. From determining some cyclotomic coset leaders $δ_i$ of cyclotomic cosets modulo $ \frac{q^m-1}{2}$, we prove that narrow-sense BCH codes of length $ \frac{q^m-1}{2}$ with designed distance $δ_i=\frac{q^m-q^{m-1}}{2}-1-\frac{q^{ \lfloor \frac{m-3}{2} \rfloor+i}-1}{2}$ have the corresponding trace representation, and have the minimal distance $d=δ_i$ and the Bose distance $d_B=δ_i$, where $1\leq i\leq \lfloor \frac{m+3}{4} \rfloor$.
Safe Coordination of Human-Robot Firefighting Teams
Venue: arXiv cs.RO 2019
Year: 2019

Wildfires are destructive and inflict massive, irreversible harm to victims' lives and natural resources. Researchers have proposed commissioning unmanned aerial vehicles (UAVs) to provide firefighters with real-time tracking information; yet, these UAVs are not able to reason about a fire's track, including current location, measurement, and uncertainty, as well as propagation. We propose a model-predictive, probabilistically safe distributed control algorithm for human-robot collaboration in wildfire fighting. The proposed algorithm overcomes the limitations of prior work by explicitly estimating the latent fire propagation dynamics to enable intelligent, time-extended coordination of the UAVs in support of on-the-ground human firefighters. We derive a novel, analytical bound that enables UAVs to distribute their resources and provides a probabilistic guarantee of the humans' safety while preserving the UAVs' ability to cover an entire fire.
Study to achieve environment adaptive software
Venue: arXiv cs.DC 2019
Year: 2019

Recently, heterogeneous hardware such as GPU and FPGA is used in many systems and also IoT devices are increased repidly. However, to utilize heterogeneous hardware, the hurdles are high because of much technical skills. In order to break down such a situation, we think it is required in the future that application programmers only need to write logics to be processed, then software will adapt to the environments with heterogeneous hardware, to make it easy to utilize heterogeneous hardware and IoT devices. Therefore, in this paper, we propose environment adaptive software to operate an once written application with high performance by automatically converting the code and configuring setting so that we can utilize GPU, FPGA and IoT devices in the location to be deployed. We explain a proceesing flow and elemental technologies to achieve environment adaptive software.
Rethinking Uplink Hybrid Processing: When is Pure Analog Processing Suggested?
Venue: arXiv cs.IT 2019
Year: 2019

In this correspondence, we analytically characterize the benefit of digital processing in uplink massive multiple-input multiple-output (MIMO) with sub-connected hybrid architecture. By assuming that the number of radio frequency (RF) chains is equal to that of users, we characterize achievable rates of both pure analog detection and hybrid detection under the i.i.d. Rayleigh fading channel model. From the derived expressions, we discover that the analog processing can outperform the hybrid processing using the maximal ratio combining (MRC) or zero-forcing (ZF) criterion in cases under some engineering assumptions. Performance comparison of the schemes are presented under tests with various numbers of users and numbers of antennas at the base station.
Architecture for Integrating Learning Platforms Using Adapter
Venue: arXiv cs.SE 2019
Year: 2019

The advantage of the electronic and mobile learning platforms is the dissemination of learning contents with ease, but these operate differently to exchange the learning the learning contents from the server to the client. integrating these learning platforms to operate as a single platform and exchange the contents based on learners' request could improve the learning efficiency and reduce the operational cost. this work introduces a Web Services approach based on the client-server model to develop an integrated architecture that join the two learning platforms. in this paper the architecture of the learning platforms is presented and explained. furthermore, an adapter in a form of Web services is developed and as a middleware for the client-server communication.
VeriSFQ - A Semi-formal Verification Framework and Benchmark for Single Flux Quantum Technology
Venue: arXiv cs.ET 2019
Year: 2019

In this paper, we propose a semi-formal verification framework for single-flux quantum (SFQ) circuits called VeriSFQ, using the Universal Verification Methodology (UVM) standard. The considered SFQ technology is superconducting digital electronic devices that operate at cryogenic temperatures with active circuit elements called the Josephson junction, which operate at high switching speeds and low switching energy - allowing SFQ circuits to operate at frequencies over 300 gigahertz. Due to key differences between SFQ and CMOS logic, verification techniques for the former are not as advanced as the latter. Thus, it is crucial to develop efficient verification techniques as the complexity of SFQ circuits scales. The VeriSFQ framework focuses on verifying the key circuit and gate-level properties of SFQ logic: fanout, gate-level pipeline, path balancing, and input-to-output latency. The combinational circuits considered in analyzing the performance of VeriSFQ are: Kogge-Stone adders (KSA), array multipliers, integer dividers, and select ISCAS'85 combinational benchmark circuits. Methods of introducing bugs into SFQ circuit designs for verification detection were experimented with - including stuck-at faults, fanout errors, unbalanced paths, and functional bugs like incorrect logic gates. In addition, we propose an SFQ verification benchmark consisting of combinational SFQ circuits that exemplify SFQ logic properties and present the performance of the VeriSFQ framework on these benchmark circuits. The portability and reusability of the UVM standard allows the VeriSFQ framework to serve as a foundation for future SFQ semi-formal verification techniques.
Discriminating Original Region from Duplicated One in Copy-Move Forgery
Venue: arXiv cs.CV 2019
Year: 2019

Since images are used as evidence in many cases, validation of digital images is essential. Copy-move forgery is a special kind of manipulation in which some parts of an image is copied and pasted into another part of the same image. Various methods have been proposed to detect copy-move forgery, which have achieved promising results. In previous methods, a binary mask determining the original and forged region is presented as the final result. However, it is not specified which part of the mask is the forged region. It should be noted that discriminating the original region from the duplicated one is not usually feasible by human visual system(HVS). On the other hand, exact localizing the forged region can be helpful for automatic forgery detection especially in combined forgeries. In real-world forgeries, some manipulations are performed in order to provide a visibly realistic scene. These modifications are usually applied on the boundary of the duplicated snippets. In this research, the texture information of the border regions of both the original and copied patches have been statistically investigated. Based on this analysis, we propose a method to discriminated copied snippets from original ones. In order to validate our method, GRIP dataset is utilized since it contains more realistic forged images which are not easily recognizable by HVS.